Aras Diagram
Structured decomposition of model behavior across bias, variability, and phase alignment, supporting clearer judgement than a single aggregate score.
Institutional climate intelligence
Climate evidence for defensible infrastructure and risk decisions.
Arasense helps infrastructure, public-sector, and advisory teams interpret climate uncertainty with structured diagnostics, model-trust evidence, forward projections, portfolio screening, and validation-stage flood analytics.
Map
Spatial console for climate points, flood boxes, and local scenario testing.
2050
Trust-weighted projections for mid-century hazard interpretation.
GNN
Topology-aware flood screening validated through regional pilots.
Research-grounded methods, framed for institutional use
Arasense is built around interpretable model evaluation and transparent evidence generation. The platform connects the Aras Diagram framework with operational geospatial data sources and validation-stage screening workflows.
ERA5-Land and CMIP6
Climate diagnostics and projections are framed around established reanalysis and global climate model datasets, with model skill made explicit.
Sentinel-1 evidence
Flood-screening pilots can be compared against satellite-derived evidence windows to document where the workflow performs and where it remains uncertain.
A private analytical console for climate-risk evidence
Arasense combines climate model evaluation, bias-aware interpretation, projection reporting, portfolio screening, and validation-stage flood analytics into one technical stack. The objective is not another generic dashboard. The objective is a more defensible basis for planning, screening, and risk communication.
Climate Intelligence
Compare climate model behavior through Aras Diagram signal decomposition, model-trust tiers, and transparent ranking logic.
Projection Reports
Estimate mid-century changes for rainfall, heat, drought, and scenario differences using trust-weighted models.
Portfolio Screening
Rank locations by worsening hazard signal so teams can see which exposures deserve attention first.
The enhanced console is built around spatial decisions
The current Arasense console is available through private demos, pilot studies, and selected collaborations. Users select climate points and flood-screening regions directly on a map, then generate structured outputs for model trust, projections, portfolio ranking, and validation review.
Map-first workflow
Click, draw, analyze
Set a climate region of interest with one map click, draw flood-screening boxes, and keep the spatial inputs synchronized across forms and reports.
Model trust
Aras Diagram plus trust engine
Score models by bias, variability, and alignment, then surface which models are credible enough to drive projections at the selected location.
Hazard reporting
2050 projections and multi-hazard profiles
Generate mid-century reports for rainfall extremes, heavy-rain frequency, heat, and drought, including SSP2-4.5 vs SSP5-8.5 comparisons.
Flood pilot
Terrain, climate, and Sentinel evidence
Combine hydrological graph structure, climate-driven precipitation features, GNN screening probabilities, and Sentinel-1 validation windows for regional pilots.
Designed for teams that need defensible climate evidence
Arasense is best suited to organizations that need climate interpretation, regional screening, and transparent evidence to support planning, prioritization, advisory work, or investment-facing communication.
Public and infrastructure
Authorities, utilities, and asset owners
Support resilience planning, regional screening, asset prioritization, and early-stage adaptation decisions with more structured evidence.
Advisory and finance
Consultancies, analysts, and risk teams
Strengthen client deliverables, due diligence workflows, and comparative climate-risk assessments with clearer interpretability.
Why the approach is credible
Scientific basis
- Aras Diagram framework for structured climate-model evaluation.
- Model Trust Engine for location-specific skill tiers and projection weights.
- Regional and multi-model assessment logic grounded in research practice.
- Focus on bias, variability, and alignment rather than presentation-only summary scores.
Technical basis
- FastAPI backend for deployable diagnostics, reports, and API workflows.
- Map-first console with climate diagnostics, projection comparison, portfolio ranking, and raw JSON validation.
- Graph neural network flood pipeline currently scoped to validation-stage regional pilots with Sentinel-1 comparison.
What clients should expect
Faster first-pass insight
Arasense is aimed at shortening the path from complex data to a first defensible interpretation, especially where teams need to compare scenarios, rank locations, or communicate uncertainty clearly.
How work can start
Pilot-led engagement
Early collaborations can begin with a geography, asset class, or decision question, then expand into repeatable workflows as the value is demonstrated.
Validation note
Flood outputs are validation-stage screening evidence. They are not a replacement for hydraulic modelling, field validation, local calibration, or engineering-grade flood forecasting.
Where Arasense can add value first
The strongest early fit is with teams that need a sharper view of climate-model reliability or a faster first-pass understanding of flood-sensitive territory in pilot geographies.
Near-term applications
Advisory and screening workflows
Climate risk screening, regional comparison, infrastructure prioritization, portfolio ranking, and client-facing evidence packs are natural early use cases.
Delivery model
Custom pilot, then platform access
Start with focused pilots or decision-support engagements, then expand into live product access and private API workflows as needs mature.
Contact
Request a private briefing or pilot discussion.
Arasense is currently shared through guided demos, pilot studies, and selected institutional collaborations.